Recognition of complex odors with a single generic tin oxide gas sensor

Hossein‐Babaei, Faramarz; Amini, Amir

doi:10.1016/j.snb.2013.12.061

Cited by 81 publications

(38 citation statements)

References 24 publications

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“…Lagrangian method is employed to solve the optimization problem is equation (10). The Lagrangian ( , ) associated with equation (10) is given by…”

Section: Discriminative Domain Regularized Component Analysis (D-drca)mentioning

confidence: 99%

Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach

2019

IEEE Access

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Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method inherits the merits of the subspace projection method called domain regularized component analysis. Moreover, the proposed method takes the source data label information into consideration, which minimizes the within-class variance of the projected source samples and at the same time maximizes the between-class variance. The label information is exploited to avoid overlapping of samples with different labels in the subspace. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach.

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“…Lagrangian method is employed to solve the optimization problem is equation (10). The Lagrangian ( , ) associated with equation (10) is given by…”

Section: Discriminative Domain Regularized Component Analysis (D-drca)mentioning

confidence: 99%

Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach

2019

IEEE Access

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“…As a mimic biological olfactory system, electronic noses (e-nose) are an intelligent multi-sensor system [ 1 , 2 ], developed by coupling a pattern recognition algorithm and gas sensor array with a certain degree of specificity and broad spectrum characteristics [ 3 ]. In recent years, e-nose techniques have been widely used in many areas, such as food quality detection [ 4 , 5 ], medical diagnostics [ 6 , 7 ], agricultural production [ 8 , 9 ], environment monitoring [ 10 , 11 , 12 ], aerospace technology [ 13 ], etc. Owing to its portability, real time operability, and ease of use, e-noses are superior to other detection methods, such as the chemical detection method, the analysis of gas chromatography and mass spectrometry (GCMS), etc.…”

Section: Introductionmentioning

confidence: 99%

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Liang

Tian

Yang

et al. 2018

Sensors

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Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc.

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“…A common tin dioxide gas sensor to identify complex odors was reported [12], which was dependent on the heating function of the device with a predetermined temperature that affected the voltage pulses to produce different odor signal differences.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

Sun

Luo

et al. 2018

Journal of Electrical and Computer Engineering

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Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO 2 , CH 4 , NH 3 , and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.

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Recognition of complex odors with a single generic tin oxide gas sensor

Cited by 81 publications

References 24 publications

Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach

Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

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